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An airline scheduling model and solution algorithms under stochastic demands

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  • Yan, Shangyao
  • Tang, Ching-Hui
  • Fu, Tseng-Chih

Abstract

When setting a good flight schedule airlines not only have to consider their fleet supply and related operations, as well as market share, but also stochastic variations caused by daily passenger demands in actual operations. Most of the past research on short-term flight scheduling has used the average passenger demand as input to produce the final timetable and schedule, which means that daily passenger variations that occur in actual operations are neglected. To consider such stochastic disturbances we developed a stochastic-demand scheduling model. We employed arc-based and route-based strategies to develop two heuristic algorithms that can be used to solve the model. The test results, based on a major Taiwan airline's operation, show the good performance of the model and the solution algorithms.

Suggested Citation

  • Yan, Shangyao & Tang, Ching-Hui & Fu, Tseng-Chih, 2008. "An airline scheduling model and solution algorithms under stochastic demands," European Journal of Operational Research, Elsevier, vol. 190(1), pages 22-39, October.
    • Handle: RePEc:eee:ejores:v:190:y:2008:i:1:p:22-39
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    References listed on IDEAS

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    Cited by:

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    3. Lo, Hong K. & An, Kun & Lin, Wei-hua, 2013. "Ferry service network design under demand uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 59(C), pages 48-70.
    4. Şafak, Özge & Çavuş, Özlem & Selim Aktürk, M., 2018. "Multi-stage airline scheduling problem with stochastic passenger demand and non-cruise times," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 39-67.
    5. Keji Wei & Vikrant Vaze, 2020. "Airline Timetable Development and Fleet Assignment Incorporating Passenger Choice," Transportation Science, INFORMS, vol. 54(1), pages 139-163, January.
    6. Vildan Özkır & Mahmud Sami Özgür, 2021. "Two-Phase Heuristic Algorithm for Integrated Airline Fleet Assignment and Routing Problem," Energies, MDPI, vol. 14(11), pages 1-10, June.
    7. Antunes, António P. & Santos, Miguel G. & Pita, João P. & Menezes, António G., 2018. "Study on the evolution of the air transport network of the Azores," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 837-851.
    8. Masood Kiarashrad & Seyed Hamid Reza Pasandideh & Mohammad Mohammadi, 2021. "A mixed-integer nonlinear optimization model for integrated flight scheduling, fleet assignment, and ticket pricing in competitive market," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(5), pages 596-607, October.
    9. Meng, Qiang & Hei, Xiuling & Wang, Shuaian & Mao, Haijun, 2015. "Carrying capacity procurement of rail and shipping services for automobile delivery with uncertain demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 38-54.
    10. N C Simpson & P G Hancock, 2009. "Fifty years of operational research and emergency response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 126-139, May.
    11. Oliveira, Beatriz B. & Carravilla, Maria Antónia & Oliveira, José F. & Costa, Alysson M., 2019. "A co-evolutionary matheuristic for the car rental capacity-pricing stochastic problem," European Journal of Operational Research, Elsevier, vol. 276(2), pages 637-655.
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